bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details of specific algorithms.

Version: 0.6-1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 0.10.6)
LinkingTo: Rcpp, RcppArmadillo
Suggests: mcmcse, coda, PottsUtils, rstan, knitr, rmarkdown, lattice
Published: 2021-04-11
DOI: 10.32614/CRAN.package.bayesImageS
Author: Matt Moores ORCID iD [aut, cre], Dai Feng [ctb], Kerrie Mengersen ORCID iD [aut, ths]
Maintainer: Matt Moores <mmoores at>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: yes
Citation: bayesImageS citation info
Materials: README NEWS
In views: Bayesian, MedicalImaging
CRAN checks: bayesImageS results


Reference manual: bayesImageS.pdf
Vignettes: Bayesian Methods for Image Segmentation


Package source: bayesImageS_0.6-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bayesImageS_0.6-1.tgz, r-oldrel (arm64): bayesImageS_0.6-1.tgz, r-release (x86_64): bayesImageS_0.6-1.tgz, r-oldrel (x86_64): bayesImageS_0.6-1.tgz
Old sources: bayesImageS archive


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